Abstract
This chapter explores the potential of thermal energy storage (TES) systems towards the decarbonization of industry and energy networks, considering its coordinated management with electrochemical energy storage and renewable energy sources (RES). It covers various TES technologies, including sensible heat storage (SHS), latent heat storage (LHS), and thermochemical energy storage (TCS), each offering unique benefits and facing specific challenges. The integration of TES into industrial parks is highlighted, showing how these systems can optimize energy manage-ment and reduce reliance on external sources. A district heating use case also demonstrates the economic and environmental advantages of a multi-energy management strategy over single-energy approaches. Overall, TES technologies are presented as a promising pathway to greater energy effi-ciency and sustainability in industrial processes.

Abstract
The application of Artificial Intelligence (AI) in the energy sector offers new opportunities for developing flexible, efficient, and sustainable infrastructures. Nevertheless, real-world deployment is still constrained by the lack of large-scale, integrated environments that can evaluate advanced algorithms under realistic operating conditions while ensuring regulatory compliance. This paper presents EnerTEF (which stands for Energy Testing and Experimentation Facility), a federated platform for testing and experimentation in the energy sector designed to address this gap. We introduce a unified TEF architecture that enables full-stack evaluation of intelligent systems, including predictive modeling, optimization, learning under data distribution shifts and federated learning across geographically distributed sites. The framework integrates high-fidelity digital twins, a privacy-preserving data exchange framework and regulatory sandboxing to support transparent, explainable and robust AI development. EnerTEF demonstrates how such a framework can be deployed in critical energy domains through three real-world scenarios including short-term hydropower generation forecasting, coordination between distribution network operators and distributed energy resources and real-time optimization of self-consumption for municipal buildings. Results show that EnerTEF effectively enables the development of novel AI models, improves cross-context generalizability and supports innovation for complex energy infrastructures, ultimately creating a practical, scalable path for addressing different energy-related problems and heterogeneous data. © 2025 The Authors.

Abstract
Ensuring reliable and high-quality electricity service is critical for consumers and Distribution System Operators (DSO). The DSO's Plan for Development and Investment in the Distribution Network (PDIDN) plays a pivotal role in enhancing network reliability and resilience while balancing technical and financial aspects. This study proposes a novel probabilistic approach for quality-of-service (QoS) estimation in distribution systems, addressing the limitations of traditional deterministic methods. Leveraging Bayesian regression, specifically the Spike and Slab technique, the model incorporates prior knowledge to improve the prediction of key QoS indicators such as SAIDI, SAIFI, and TIEPI. Using historical network data, the model demonstrates superior predictive accuracy and robustness, offering realistic confidence intervals for strategic planning. This method enables informed investments, enhances regulatory compliance, and supports renewable integration. The findings underline the potential of probabilistic modeling in advancing QoS forecasting, encouraging its application in other areas of electric network management.

Abstract
This article explores the optimization of Battery Energy Storage Systems (BESS) in energy markets, emphasizing their role in decarbonization by storing excess renewable energy and mitigating grid constraints. BESS enables energy transition by facilitating energy arbitrage, frequency regulation, and grid stabilization, essential for integrating variable renewable sources. Focusing on the UK energy market, the study highlights the favorable policies and investments driving BESS deployment. It examines revenue streams, including Day-Ahead and Intraday markets, ancillary services, and balancing mechanisms, particularly dynamic services like frequency regulation. Challenges such as gas market volatility and regulatory hurdles are also discussed. The proposed market optimization model simulates BESS operations, revealing consistent revenue potential influenced by market conditions and regulatory frameworks. The study underscores BESSs critical role in stabilizing grids, supporting renewables, and enhancing energy security while calling for further research on equipment degradation and broader impacts on energy systems and pricing.

Abstract
As electric vehicle (EV) adoption accelerates, residential buildings-particularly multi-dwelling structures-face increasing challenges to electrical infrastructure, notably due to conservative sizing practices of electrical feeders based on maximum simultaneous demand. Current sizing methods assume all EVs charge simultaneously at maximum capacity, resulting in unnecessarily oversized and costly electrical installations. This study proposes an optimized methodology to estimate accurate coincidence factors, leveraging simulations of EV user charging behaviors in multi-dwelling residential environments. Charging scenarios considering different fleet sizes (1 to 70 EVs) were simulated under two distinct premises of charging: minimization of current allocation to achieve the desired battery state-of-charge and maximization of instantaneous power delivery. Results demonstrate significant deviations from conventional assumptions, with estimated coincidence factors decreasing non-linearly as fleet size increases. Specifically, applying the derived coincidence factors can reduce feeder section requirements by up to 86%, substantially lowering material costs. A fuzzy logic inference model is further developed to refine these estimates based on fleet characteristics and optimization preferences, providing a practical tool for infrastructure planners. The results were compared against other studies and real-life data. Finally, the proposed methodology thus contributes to more efficient, cost-effective design strategies for EV charging infrastructures in residential buildings.

Abstract
Battery degradation remains a major challenge in electric vehicle (EV) adoption, directly affecting long-term performance, cost, and user satisfaction. This paper proposes a data-driven charging strategy that reduces battery wear while meeting the user's daily range needs. By integrating manufacturer guidelines, battery aging models, and thermal dynamics, the proposed optimization algorithm dynamically adjusts the charging current and timing to minimize stressors, such as high temperatures and prolonged high state of charge (SoC). The methodology is responsive to user inputs such as departure time and required driving range, enabling personalized charging behavior. Simulation results show that this approach can reduce battery degradation by up to 2.7% over a 30-day period compared to conventional charging habits, without compromising usability. The framework is designed for integration into Battery Management Systems (BMS), with applications for both private EV users and fleet operators. We address EV battery aging driven by high core temperature and prolonged high state of charge (SoC) during overnight/home charging. Given a user-specified departure time and required driving range, we schedule charging power over time to minimize predicted degradation exposure while still meeting the range requirement. The scheduler optimizes charging timing/current under SoC dynamics, thermal constraints, and charger/ BMS limits. © 2013 IEEE.